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• W2734656543 abstract "This study was conducted to create driving episodes using machine-learning-based algorithms that address long-term memory (LTM) and topological mapping. This paper presents a novel episodic memory model for driving safety according to traffic scenes. The model incorporates three important features: adaptive resonance theory (ART), which learns time-series features incrementally while maintaining stability and plasticity for time-series data; self-organizing maps (SOMs), which represent input data as a map with topological relations using self-mapping characteristics; and counter propagation networks (CPNs), which label category maps using input features and counter signals. Category maps represent driving episode information that includes driving contexts and facial expressions. The bursting states of respective maps produce LTM, which is created on ART as episodic memory. Evaluation of the experimentally obtained results show the possibility of using recorded driving episodes with image datasets obtained using an event data recorder (EDR) with two cameras. Using category maps, we visualize driving features according to driving scenes on a public road and an expressway." @default.
• W2734656543 created "2017-07-21" @default.
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• W2734656543 date "2017-05-01" @default.
• W2734656543 modified "2023-09-26" @default.
• W2734656543 title "Adaptive learning based driving episode description on category maps" @default.
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• W2734656543 doi "https://doi.org/10.1109/ijcnn.2017.7966247" @default.
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